Anomalous Ambiguity Detection Between Reference Stations Based on Box-Cox Transformation of Tropospheric Residual Estimation

Abstract

Abstract With the increasing scale and complexity of network RTK, the reliability of ambiguity resolution becomes particularly crucial. Undetected fixed errors may trigger a chain reaction in subsequent atmospheric delay extraction and fitting stages, thereby affecting the reliability of user positioning services. Current methods for checking abnormal ambiguities suffer from issues such as inflexible threshold selection, excessive exclusion, and overlooking observational anomalies. Addressing these concerns, this paper proposes a reference station-based abnormal ambiguity detection method utilizing tropospheric residual estimation, named Chi-Square Test combining Box-Cox Transformation and Secondary Screening (BS-CT). By unitizing the residual estimates of zenith tropospheric values obtained through ambiguity resolution and applying Box-Cox transformation for standard normalization, they become suitable for chi-square testing, thereby eliminating satellites with fixed ambiguity anomalies. The integration of secondary screening strategy further ensures the reliable fixation of the ambiguity quantity. Experimental results demonstrate that the BS-CT method, compared to Ordinary Chi-Square Test (O-CT), Partial Ambiguity Resolution (PAR) methods and a method utilizing a decision function g for enhanced fixed fraction and variance strategy, exhibits lower false alarm rates and comparable missed detection rates to PAR method. Thus, the BS-CT method demonstrates robust abnormal ambiguity detection while ensuring a reliable quantity of fixed ambiguities.